Unified mmse equalization and multi-user detection approach for use in a cdma system

ABSTRACT

A unified minimum mean square error (MMSE) equalization/multi-user detection (MUD) approach for demodulating direct sequence CDMA (DS-CDMA) signals is described. In at least one embodiment, the unified approach is capable of generating a variety of cost-effective receiver demodulation techniques that may range from, for example, a low cost linear MMSE equalization technique to a relatively high complexity MMSE MUD.

RELATED APPLICATION DATA

The present application is a continuation of U.S. patent applicationSer. No. 10/697,853 filed Oct. 30, 2003, and entitled “A Unified MMSEEqualization and Multi-User Detection Approach for Use in a CDMASystem,” which is hereby incorporated by reference herein in itsentirety.

FIELD OF THE INVENTION

The invention relates generally to wireless communications and, moreparticularly, to methods and structures for detecting data within awireless system.

BACKGROUND OF THE INVENTION

Code division multiple access (CDMA) is a multiple access technique inwhich a plurality of substantially orthogonal codes (usually taking theform of pseudo-random noise sequences) are used to spread spectrummodulate user signals within a system. Each modulated user signal mayhave an overlapping frequency spectrum with other modulated user signalsin the system. However, because the underlying modulation codes areorthogonal, it is possible to demodulate individual user signals byperforming a correlation operation using the appropriate code. As can beappreciated, a communication device operating within a CDMA-based systemwill often receive overlapping communication signals associated with avariety of different users. The signals associated with other users willtypically appear as interference when trying to demodulate a signalassociated with a desired user within the communication device. There isan ongoing need for techniques and structures for efficiently and/oraccurately demodulating user signals in a CDMA environment.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating an example receiver arrangementin accordance with an embodiment of the present invention;

FIG. 2 is a block diagram illustrating an example joint equalization andMUD device in accordance with an embodiment of the present invention;

FIG. 3 is a block diagram illustrating an example receiver arrangementthat may be used to represent the family of MUD receivers in accordancewith an embodiment of the present invention;

FIG. 4 is a block diagram illustrating an example receiver arrangementin accordance with an embodiment of the present invention, that may beused to implement interference cancellation techniques; and

FIG. 5 is a block diagram illustrating an example method for use in aCDMA receiver in accordance with an embodiment of the present invention.

DETAILED DESCRIPTION

In the following detailed description, reference is made to theaccompanying drawings that show, by way of illustration, specificembodiments in which the invention may be practiced. These embodimentsare described in sufficient detail to enable those skilled in the art topractice the invention. It is to be understood that the variousembodiments of the invention, although different, are not necessarilymutually exclusive. For example, a particular feature, structure, orcharacteristic described herein in connection with one embodiment may beimplemented within other embodiments without departing from the spiritand scope of the invention. In addition, it is to be understood that thelocation or arrangement of individual elements within each disclosedembodiment may be modified without departing from the spirit and scopeof the invention. The following detailed description is, therefore, notto be taken in a limiting sense, and the scope of the present inventionis defined only by the appended claims, appropriately interpreted, alongwith the full range of equivalents to which the claims are entitled. Inthe drawings, like numerals refer to the same or similar functionalitythroughout the several views.

The commonly used RAKE receiver is known to be an optimal solution fordemodulating a CDMA signal received from an additive white Gaussiannoise (AWGN) channel. However, in a multiple access interference (MAI)environment (as encountered in, for example, cellular CDMA systems), theRAKE receiver is not optimal and in some cases is very far from theoptimal solution. In recent years, significant efforts have been devotedto developing advanced CDMA receiver techniques in order to improve CDMAnetwork performance. Multi-user detection (MUD) techniques, in which thereceiver jointly demodulates users (i.e., the desired user signal aswell as the other interfering user signals), are one example of anadvanced CDMA receiver technology. MUD techniques are capable ofsignificantly improving demodulation performance by utilizing knowledgeabout the MAI associated with the jointly demodulated users. Channelequalization techniques that are applied at the chip domain representanother advanced receiver technology that is applicable only toorthogonal CDMA. The key idea in chip-rate equalization techniques is toequalize the communication channel to become close to a unitarytransformation, thus restoring the orthogonality of the transmittedsignal that was destroyed in the channel. By doing so, cross-talkbetween different users (of the same base station) is reduced becausethe orthogonality of the signature codes that was compromised in thechannel is at least partially restored. In cellular systems, this methodmay be applicable only to down-link users of the active base station asthese are typically the only users that are orthogonal to the desireduser.

In at least one embodiment of the present invention, a unifiedMUD-equalization approach is provided for demodulating direct sequenceCDMA (DS-CDMA) signals. The unified approach is capable of generating avariety of cost-effective receiver demodulation techniques that mayrange from, for example, a low cost linear minimum mean square error(MMSE) equalization technique (which may provide the lowest performancegains compared to the RAKE receiver) to a relatively high complexityMMSE MUD (which may provide the largest performance gains). In between,various performance/complexity tradeoffs may be chosen. Hereinafter, theterm “equalization” will be used to represent optimal means square error(MSE) processing that is “signature independent” and thus may beextended to include signals from other base stations that are notorthogonal to a desired user.

FIG. 1 is a block diagram illustrating an example receiver arrangement10 in accordance with an embodiment of the present invention. Asillustrated, the receiver arrangement 10 may include at least one of: anantenna 12, a radio frequency (RF) to baseband subsystem 14, a jointequalization and MUD unit 16, and a channel decoder 18. The antenna 12may receive a composite CDMA signal from a wireless channel 10 thatincludes overlapping signal components associated with multiple users inthe system. In addition to a desired user, the composite signal mayinclude signal components from other users associated with the same basestation as the desired user and/or other users associated with otherbase stations. Any form of antenna 12 may be used including, forexample, a dipole, a patch, a helix, an array, and/or others, includingcombinations of the above. In at least one embodiment, antenna diversitytechniques are used. The RF to baseband subsystem 14 converts thereceived signal from an RF representation to a baseband representation.The RF to baseband subsystem 14 may include components such as, forexample, a low noise amplifier, one or more RF filters, one or morefrequency conversion devices (e.g., mixers, etc.), and/or any othercomponents needed to convert the received signal to baseband. As will bedescribed in greater detail, the joint equalization and MUD device 16 isoperative for detecting data within the composite received signal thatis associated with the desired user, using a unified MUD/equalizationapproach. The channel decoder 18 decodes the detected data, based on apredetermined channel code, to recover the user data for the desireduser. Any form of channel code may be used. In at least one embodiment,no channel coding is used.

FIG. 2 is a block diagram illustrating an example joint equalization andMUD device 30 in accordance with an embodiment of the present invention.The joint equalization and MUD device 30 may be used within, forexample, the receiver arrangement 10 of FIG. 1 and/or within CDMAreceivers having other architectures. As illustrated, the jointequalization and MUD device 30 includes: a sampler 32, a time trackingunit 34, a despreader 36, and a joint equalizer/MUD despreading sequencegenerator 38. The despreader 36 includes a multiplier 40 and anaccumulator 42. The time tracking unit 34 causes the sampler 32 tosample the received baseband samples at the chip rate to generate chiprate samples y_(k). The despreader 36 despreads the received signal bymultiplying (i.e. within the multiplier 40) the chip rate samples by adespreading sequence generated by the joint equalizer/MUD despreadingsequence generator 38 and then summing (i.e., within the accumulator 42)the result over the symbol period to generate a desired user symbol â atan output thereof. The desired user symbols may then be delivered to achannel decoder for decoding. In at least one embodiment of theinvention, as will be described in greater detail, a despreadingsequence may be generated by the joint equalizer/MUD despreadingsequence generator 38 that can combine equalization and MUD processingin a manner that can achieve enhanced despreading performance while alsoaddressing computational complexity concerns.

In at least one implementation, a despreading sequence may be generatedby first dividing the current users in a system into two groups. A firstgroup (i.e., group 1) consists of users whose signature sequences are“assumed” to be known to the receiver and a second group (i.e., group 2)consists of users whose signature sequences are “assumed” to be unknownto the receiver and whose first and second order statistics comply withthose of the true signals. The number of users in group 1 may be denotedas K and the number of users in group 2 may be denoted as L (or L+1,where the +1 is used to represent additive white noise in the system).As will be described in greater detail, when K and L are varied,different receiver architectures and performance can be achieved. Usersin group 1 will generally be treated using MUD-type processing. Thisprocessing will typically result in higher performance at the expense ofhigher computational complexity. Users in group 2, on the other hand,will generally be treated using equalization-type processing, which willtypically result in lower performance with less computationalcomplexity. In general, as the number of users assigned to group 2increases, the overall performance will degrade and the overallcomputational complexity will decrease.

It should be appreciated that, in at least one embodiment of theinvention, the decision as to which group a particular user is placed indoes not necessarily depend upon whether or not the signature of thatuser is actually known in the receiver. In fact, in many situations, areceiver may know the signatures of all current users. Instead, thedecision may be based upon a tradeoff between performance andcomputational complexity in the receiver. That is, a decision may bemade to treat certain users in one way and other users in another way toachieve a tradeoff between performance and complexity. In one approach,for example, all users associated with the same base station as adesired user may be placed in group 1, while all users associated withother base stations are placed in group 2 (i.e., even if theirsignatures are known to the receiver). In this manner, overallcomputational complexity may be reduced by limiting application of MUDprocessing to users associated with the serving base station (i.e., thebase station associated with the desired user). Other techniques forassigning the users to the two groups may alternatively be used. In atleast one embodiment, the technique that is used to assign users to thetwo groups may change over time (e.g., it may be definable and/ormodifiable by a primary user of a receiver, etc.).

As described above, in at least one implementation, various differentreceiver architectures may be achieved by varying the values of K and Lin a receiver. For example, if K=1 and L=0, the well known RAKE receivermay be achieved (with optimized weights). The maximal-ratio combining(MRC) RAKE receiver may be obtained as a special case when the signal tonoise ratio (SNR) approaches zero (in any other case, MRC is known to besub-optimal). In another example, if K=1 and L=the number of users inthe serving base station−1, the conventional MMSE equalizer may beachieved. In yet another example, if K=all current users, then the fullblown MUD receiver may be achieved. In addition, by selecting K and L ina different manner from the above examples, a variety of cost effectivejoint MUD-equalization receiver techniques may be achieved that are ableto handle both inter-symbol interference (ISI) and multiple accessinterference (MAI) optimally (under the MSE criterion) for a given levelof complexity. In this manner, a performance/complexity tradeoff may beachieved.

In the discussion that follows, a general technique for determining ajoint equalizer/MUD despreading sequence is described. In a CDMAreceiver, the baseband equivalent received signal may be represented asfollows:

$\begin{matrix}{{y(t)} = {{\sum\limits_{k = 0}^{K - 1}{{s_{k}(t)} \otimes {h_{k}(t)}}} + {\sum\limits_{l = 0}^{L - 1}{{n_{l}(t)} \otimes {g_{l}(t)}}}}} & \left( {{Equation}\mspace{14mu} 1} \right)\end{matrix}$

where s_(k)(t) are the DS-CDMA signals whose signatures are assumed tobe known to the receiver (without loss of generality s₀(t) is assumedthroughout to be the desired signal), h_(k)(t) is the k-th channelimpulse response (i.e., the impulse response that the k-th DS-CDMAsignal propagates through, including the transmitter and receiverfiltering effects), {circle around (×)} denotes the convolutionoperator, n_(l)(t) is a white noise process, and g₁(t) is an arbitraryimpulse response whose characterization is provided below. Let g₀(t) beequal to the impulse response of the receiver filter. Therefore, thefirst term in the second summation of Equation 1 is just thecontribution of a white noise process to the baseband signal (e.g., thethermal noise of the receiver chain). The other n_(l)(t) terms are usedto represent DS-CDMA signals whose signatures are assumed to be unknownto the receiver, with g₁(t) representing their respective channels(which are assumed known to the receiver). It should be reiterated thatthe distinction between CDMA users whose signature is assumed known tothose whose signature is assumed unknown may be relevant as it relatesdirectly to the cost/performance tradeoffs. It should also be noted thatconventional random spreading assumptions and the fact that thechip-shaping is embedded in g₁(t) immediately imply the temporalwhiteness of n_(l)(t). The resulting overall signal n_(l)(t){circlearound (×)}g₁(t) as just defined, complies in its first and second-orderstatistics (which is what is needed for MSE analysis) with randomlyspread DS-CDMA signals. Furthermore, statistical independence betweenthe data streams of the different users results in statisticalindependence between the different signals (i.e., s_(k)(t)'s andn_(l)(t)'s). Thus, the following two assumptions may be made regardingthe signals in Equation 1, which are sufficient for all of thederivations that follow:

Assumption 1—Lack of correlation between the different users:

E{s _(k) ₁ (t)·s _(k) ₂ (t−τ)*}=0, ∀k₁≠k₂, ∀τ;

E{n _(k) ₁ (t)·n _(k) ₂ (t−τ)*}=0, ∀k₁≠k₂, ∀τ;

E{s _(k) ₁ (t)·n _(k) ₂ (t−τ)}=0, ∀k₁, k₂, τ  (Equation 2)

Assumption 2—Temporal whiteness of the DS-CDMA signals whose signaturesare unknown to the receiver:

E{n _(k)(t)·n _(k)(t−τ)*}=0 ∀k, ∀τ·0  (Equation 3)

Solely for purposes of convenience, it is assumed that the symbolsequences of the different users are uncorrelated both temporally andbetween the users. However, it should be stressed that this assumptionis merely for convenience and that it would be straightforward to derivethe solution for other symbol distributions.

The simple format of Equation 1 allows different receiver architecturesto be investigated by appropriately defining the various parametersappearing in the equation and deriving the MMSE solution. In thefollowing discussion, a general MMSE solution is first derived for themodel of Equation 1. Then, it is shown how different receiverarchitectures may be derived from the general solution. The MMSE outputfor the n-th symbol (denoted by â₀ (n)) may be expressed as follows:

$\begin{matrix}{{{\hat{a}}_{0}(n)} = {\sum\limits_{j = m_{1}}^{m_{2}}{{w_{j}^{*}(n)} \cdot {y\left( {j \cdot T_{s}} \right)}}}} & \left( {{Equation}\mspace{14mu} 4} \right)\end{matrix}$

where T_(s) is the sampling interval at the receiver (which is normallyan integer fraction of T_(c) the chip period), and jεm₁, m₁+1, . . . ,m₂ is an appropriate pre-defined observation window centered around then-th symbol. Typically, (m₂−m₁)·T_(s) would be of the same order as thespreading factor (SF) multiplied by T_(c), although somewhat larger. Theexact value depends on the delay spread of the channel and may be a userdefinable parameter dictated by performance/complexity tradeoffs. Whenthe delay spread of the channel is larger than the symbol duration, itmay be desirable to let the observation window span more than thesymbol. In such a setting, the proposed approach also mitigates theinter-symbol interference (ISI) generated by the channel. This may be ofparticular importance for low spreading factors which are becoming moreand more popular for high data rate applications. In vector notation,Equation 4 becomes:

â ₀(n)= W _(n) ⁺ · Y _(n)  (Equation 5)

As is well known, the MMSE solution for W _(n) is given by:

W _(n) =E{ Y _(n) · Y _(n) ⁺}⁻¹ ·E{ Y _(n) ·a ₀(n)*}  (Equation 6)

where a₀(n) denotes the desired user symbol. Using Equation 1 andAssumption 1, it can be shown that:

E{Y _(n) ·a ₀(n)*}= P _(eq) _(—) ₀(n)·E{|a ₀(n)|²}  (Equation 7)

where P _(eq) _(—) ₀(n) is the equivalent spreading sequence of thedesired symbol taking into account the channel effects (see Equation 8and Equation 9 below). Let c_(k)(i) denote the chip sequence of the k-thuser and define:

$\begin{matrix}{{P_{eq\_ k}\left( {t,n} \right)} = {\sum\limits_{i = {{n \cdot {SF}} + 1}}^{{({n + 1})} \cdot {SF}}{{c_{k}(i)} \cdot {h_{k}\left( {t - {i \cdot T_{c}}} \right)}}}} & \left( {{Equation}\mspace{14mu} 8} \right)\end{matrix}$

Then, the elements of P _(eq) _(—) ₀(n) are taken to be the T_(s) spacedsamples of the “equivalent” spreading sequence of the desired usercorresponding to the respective symbol a₀(n) (i.e., substitute k=0 andt=m₁·T_(s), (m₁+1)·T_(s), . . . , m₂·T_(s) in Equation 8 above). Thismay be expressed mathematically as:

$\begin{matrix}{{\left\{ {{\overset{\_}{P}}_{{eq\_}0}(n)} \right\}_{j} = {\sum\limits_{i = {{n \cdot {SF}} + 1}}^{{({n + 1})} \cdot {SF}}{{c_{0}(i)} \cdot {h_{0}\begin{pmatrix}{\left( {j + m_{1} - 1} \right) \cdot} \\{T_{s} - {i \cdot T_{c}}}\end{pmatrix}}}}}{{j = 1},2,\ldots \mspace{14mu},\left( {m_{2} - m_{1} + 1} \right)}} & \left( {{Equation}\mspace{14mu} 9} \right)\end{matrix}$

which could also be expressed in matrix notation as:

P _(eq) _(—) ₀(n)=H ₀ · C ₀(n)  (Equation 10)

where H₀ is the Toeplitz matrix whose i,j-th element is h₀((i+m₁−1)·T_(s)−j·T_(c)) and the i-th element of the chip-vector C ₀(n)is the i-th chip, c₀(i). It is noted that Equation 7 through Equation 9follow from Equation 1 by utilizing the following conventional DS-CDMAmodel for s_(k)(t):

$\begin{matrix}{{s_{k}(t)} \equiv {\sum\limits_{i = {- \infty}}^{\infty}{{a_{k}\left( \left\lfloor \frac{i - 1}{SF} \right\rfloor \right)} \cdot {c_{k}(i)} \cdot {\delta \left( {t - {i \cdot T_{c}}} \right)}}}} & \left( {{Equation}\mspace{14mu} 11} \right)\end{matrix}$

where └x┘ denotes the integer part of x (and the symbol index), δ(t)represents the Dirac delta function, and noting that:

Y _(n) =P _(eq)(n)· a (n)+ē(n)  (Equation 12)

where P_(eq) is the (m₂−m₁+1)×{tilde over (K)} matrix whose k-th columnis P _(eg) _(—) _(k)(n) (i.e., the vector obtained by calculatingEquation 8 for t=m₁·T_(s), (m₁+1)·T_(s), . . . , m₂·T_(s)) and ā is the{tilde over (K)}×1 vector of symbols associated with the different“users.”

Due to edge effects (e.g., when the observation window spans more than asingle symbol period or when the channel delay spread is non-zero), thevector a may need to contain several consecutive symbols for each user(e.g., the n−1, n, and n+1 symbols per user). Otherwise, the model ofEquation 12 may hold only approximately (up to the edge effects). Whenthe channel delay spread is smaller than the symbol period (which isoften the case) and the observation window is not too large, the abovethree consecutive symbols will typically be sufficient to assurenegligible edge effects. In this case, the additional symbols areconsidered as additional “users,” hence {tilde over (K)}≧K (e.g., whenthe n−1, n, and n+1 symbols are used, then K=3·K). Consequently, P _(eq)_(—) _(k)(n) may contain zeros corresponding to those sampling instanceswhere the relevant symbol has no effect. Finally, ē is the vector whoseelements are

${{\sum\limits_{l = 0}^{L - 1}{{{n_{l}(t)} \otimes {g_{l}(t)}}\mspace{14mu} {at}\mspace{14mu} t}} = {m_{1} \cdot T_{s}}},{\left( {m_{1} + 1} \right) \cdot T_{s}},\ldots \mspace{14mu},{m_{2} \cdot {T_{s}.}}$

From Equation 12, it may be shown that E{ Y· Y ⁺} can be calculated asfollows:

$\begin{matrix}\begin{matrix}{{E\left\{ {{\overset{\_}{Y}}_{n} \cdot {\overset{\_}{Y}}_{n}^{+}} \right\}} = {{{P_{eq}(n)} \cdot {Diag}}\left\{ {{E\left\{ {{a_{0}(n)}}^{2} \right\}},\ldots \mspace{14mu},} \right.}} \\{{\left. {E\left\{ {{a_{\overset{\sim}{K} - 1}(n)}}^{2} \right\}} \right\} \cdot {P_{eq}(n)}^{+}} + {E\left\{ {\overset{\_}{e} \cdot {\overset{\_}{e}}^{+}} \right\}}} \\{= {\sum\limits_{k = 0}^{\overset{\sim}{K} - 1}{E{\left\{ {{a_{k}(n)}}^{2} \right\} \cdot {{\overset{\_}{P}}_{eq\_ k}(n)} \cdot}}}} \\{{{{\overset{\_}{P}}_{eq\_ k}(n)}^{+} + {\sum\limits_{l = 0}^{L - 1}{N_{l} \cdot T_{l}}}}}\end{matrix} & \left( {{Equation}\mspace{14mu} 13} \right)\end{matrix}$

where N_(l) is the power spectral density of n_(l)(t) and T₁ is theToeplitz matrix whose i, j-th element is:

$\begin{matrix}\begin{matrix}{\left\{ T_{l} \right\}_{i,j} \equiv {\int{{{g_{l}\left( {{\left\lbrack {i - j} \right\rbrack \cdot T_{s}} + t} \right)} \cdot {g_{l}(t)}^{+}}{t}}}} \\{= {\frac{1}{2 \cdot \pi}{\int{{{{G_{l}(w)}}^{2} \cdot ^{j\; {w{({i - j})}}T_{s}}}{w}}}}}\end{matrix} & \left( {{Equation}\mspace{14mu} 14} \right)\end{matrix}$

It is well known that a chip-spaced sampling of the matched filteroutput (i.e., the receiver filter that is matched to the transmittershaping filter having its output sampled at the chip rate) constitutessufficient statistics for the estimation of a₀(n) in a variety ofsettings. Hence, when chip spaced sampling is performed, the followingmay be substituted in Equation 14 to simplify the derivations:

T_(s)=T_(c)  (Equation 15)

Since practical receiver implementations often utilize higher samplingrates than the chip rate (typically for the purpose of time-trackingand/or for simplifying the requirements from the anti-aliasing analogfilters and analog-to-digital converters (A/D's), such a substitutionmay not be desirable. Nevertheless, the demodulation itself is oftenperformed at the chip rate and not at the higher sampling rate (e.g.,the RAKE fingers). It is also noted that the receiver filter is oftensimply a unit-gain low pass filter (LPF) of bandwidth ½·T_(c) (i.e.,one-sided) or some other Nyquist filter (e.g., a square-root raisedcosine, etc.), in which case it may be shown from Equation 15 that:

$\begin{matrix}{T_{0} \equiv {\frac{1}{T_{c}} \cdot I}} & \left( {{Equation}\mspace{14mu} 16} \right)\end{matrix}$

which follows from the fact that the chip-spaced correlation sequence ofthe receiver filter is obviously a Kronecker delta function in thesecases (recall that g₀(t) was set to be the impulse response of thereceiver filter and Equation 16 simply implies that after receiverfiltering, the variance of the white noise term is N₀/T_(c), asexpected).

While not directly related to equalization and/or MUD, it is stilluseful to examine the RAKE receiver in the context of the presentinvention. To do this, a minimal amount of knowledge will be assumed atthe receiver. That is, it is assumed that only the signature of thedesired user is known (i.e., K=1) and nothing is known about theinterference which is modeled to be white noise (i.e., L=1). It is alsoassumed that chip-rate sampling is being used (i.e., Equation 15). UsingEquations 6, 7, 8, 13, and 16, it may be shown that the solution isgiven by:

$\begin{matrix}\begin{matrix}{{\overset{\_}{w}}_{n} = {E{\left\{ {{\overset{\_}{Y}}_{n} \cdot {\overset{\_}{Y}}_{n}^{+}} \right\}^{- 1} \cdot E}\left\{ {{\overset{\_}{Y}}_{n} \cdot {a_{0}(n)}^{*}} \right\}}} \\{= {\left\lbrack {{\sum\limits_{k \in \Omega_{0}}{{{\overset{\_}{P}}_{eq\_ k}(n)} \cdot {{\overset{\_}{P}}_{eq\_ k}^{+}(n)}}} + {\frac{N_{0}/T_{c}}{E{\left\{ {{a_{0}(n)}}^{2} \right\} \cdot}} \cdot I}} \right\rbrack^{- 1} \cdot}} \\{{{\overset{\_}{P}}_{{eq\_}0}(n)}}\end{matrix} & \left( {{Equation}\mspace{14mu} 17} \right)\end{matrix}$

where Ω₀ is the set representing the “user” indexes corresponding tosymbols of the desired user (i.e., the users whose symbols are . . . ,a₀(n−1), a₀(n), a₀(n+1), . . . (obviously 0εΩ₀)).

Next, it is assumed that E{|a₀(n)|²}<<N₀/T_(c) (i.e., the pre-despreaduser power is small compared to the total noise power, as is the case inCDMA systems where each user is normally assigned a small portion of thetotal transmitted power), in which case Equation 17 can be approximatedby:

$\begin{matrix}{{\overset{\_}{w}(n)} \approx {\frac{E\left\{ {{a_{0}(n)}}^{2} \right\}}{N_{0}/T_{c}} \cdot {{\overset{\_}{P}}_{{eq\_}0}(n)}}} & \left( {{Equation}\mspace{14mu} 18} \right)\end{matrix}$

that apart for the gain term (which is irrelevant for demodulationperformance) is exactly a matched filter to the desired user signaturesequence and coincides with the well known rake receiver (which isin-fact a matched filter).

It is interesting to note that the exact MMSE solution for the situationgiven in Equation 17 mitigates self-MAI (i.e., the first term in thebracketed expression) and is therefore expected to provide betterperformance compared with the RAKE receiver. However, this receiver ismore complicated than the RAKE and the expected performance improvementmay not justify the added cost.

The simplest receiver structure apart from the above-describedconventional RAKE receiver is the chip-rate equalizer. To derive thisequalizer, it is assumed that the receiver mitigates the MAI of theserving base station via equalization and treats all other users in thesystem as white noise. Thus, the following parameters may be used inEquation 1: K=1 (i.e., the receiver is assumed to know only thesignature of the desired user) and L>1 (and, in this setting, L equalsthe number of users in the active base). Also, it may be assumed that:

g _(l)(t)=h ₀(t), l=1, . . . , L  (Equation 19)

That is, that all of the interfering signals undergo the same channel asthe desired signal (which is the case for all down-link users of theserving base station). All other users that are not associated with theserving base station are modeled as white Gaussian noise (i.e., n₀(t)models out-of-cell MAI). It is also assumed that chip-rate sampling isbeing used (i.e., Equation 15).

Based on the above, the optimal MMSE solution is given by substitutingthese parameters into Equations 6, 7, and 8, which results in thefollowing:

$\begin{matrix}\begin{matrix}{{\overset{\_}{w}}_{n} = {E{\left\{ {{\overset{\_}{Y}}_{n} \cdot {\overset{\_}{Y}}_{n}^{+}} \right\}^{- 1} \cdot E}\left\{ {{\overset{\_}{Y}}_{n} \cdot {a_{0}(n)}^{*}} \right\}}} \\{= \left\lbrack {{\sum\limits_{k \in \Omega_{0}}{{{\overset{\_}{P}}_{eq\_ k}(n)} \cdot {{\overset{\_}{P}}_{eq\_ k}^{+}(n)}}} + {\sum\limits_{l = 1}^{L}{\frac{N_{l}}{E\left\{ {{a_{0}(n)}}^{2} \right\}} \cdot}}} \right.} \\{\left. {T_{L} + {\frac{N_{0}/T_{c}}{E\left\{ {{a_{0}(n)}}^{2} \right\}} \cdot I}} \right\rbrack^{- 1} \cdot {{\overset{\_}{P}}_{{eq\_}0}(n)}}\end{matrix} & \left( {{Equation}\mspace{14mu} 20} \right)\end{matrix}$

where T_(L) is the channel's correlation Toeplitz matrix (which isobtained by substituting Equation 19 into Equation 14). Again, the firstterm in the bracketed expression represents combating self-MAI. If weassume that the (re-despread) user power is small compared to the(pre-despread) total power, then Equation 20 reduces to:

$\begin{matrix}\begin{matrix}{{\overset{\_}{w}}_{n} = {E{\left\{ {{\overset{\_}{Y}}_{n} \cdot {\overset{\_}{Y}}_{n}^{+}} \right\}^{- 1} \cdot E}\left\{ {{\overset{\_}{Y}}_{n} \cdot {a_{0}(n)}^{*}} \right\}}} \\{\approx {\left\lbrack {{\sum\limits_{l = 1}^{L}{\frac{N_{l}}{E\left\{ {{a_{0}(n)}}^{2} \right\}} \cdot T_{L}}} + {\frac{N_{0}/T_{c}}{E{\left\{ {{a_{0}(n)}}^{2} \right\} \cdot}} \cdot I}} \right\rbrack^{- 1} \cdot}} \\{{{\overset{\_}{P}}_{{eq\_}0}(n)}} \\{= {\left\lbrack {{\sum\limits_{l = 1}^{L}{\frac{N_{l}}{E\left\{ {{a_{0}(n)}}^{2} \right\}} \cdot T_{L}}} + {\frac{N_{0}/T_{c}}{E{\left\{ {{a_{0}(n)}}^{2} \right\} \cdot}} \cdot I}} \right\rbrack^{- 1} \cdot}} \\{{H_{0} \cdot {{\overset{\_}{C}}_{0}(n)}}}\end{matrix} & \left( {{Equation}\mspace{14mu} 21} \right)\end{matrix}$

where Equation 10 was invoked in the transition to the second row ofEquation 21. Note that the only dependence on the symbol index isthrough the vector of chips of the desired user. Therefore, thisreceiver can be implemented by first applying the channel-dependent (butsymbol independent) transformation:

$\begin{matrix}{{\overset{\_}{Z}}_{n} \equiv {H_{0}^{+} \cdot \begin{bmatrix}{{\sum\limits_{l = 1}^{L}{\frac{N_{l}}{E\left\{ {{a_{0}(n)}}^{2} \right\}} \cdot T_{L}}} +} \\{\frac{N_{0}/T_{c}}{E{\left\{ {{a_{0}(n)}}^{2} \right\} \cdot}} \cdot I}\end{bmatrix}^{- 1} \cdot {\overset{\_}{Y}}_{n}}} & \left( {{Equation}\mspace{14mu} 22} \right)\end{matrix}$

followed by a simple despreader:

â ₀(n)≡ C ₀(n)⁺ · Z _(n)  (Equation 23)

In order to gain some insight into the operation of this receiver, it isnoted that T_(L) is the channel auto-correlation function (see Equation19 and Equation 14). Thus, up to edge effects can be approximated (atleast for exponentially stable channels) by:

$\begin{matrix}{T_{L} \cong {\frac{1}{T_{c}}{H_{0} \cdot H_{0}^{+}}}} & \left( {{Equation}\mspace{14mu} 24} \right)\end{matrix}$

To verify Equation 24 for, for example, the commonly used chip-spacedmultipath channel model, the channel frequency response (which is a sumof exponents) is simply substituted into the second row of Equation 14and the integration is performed (recalling that receiver filteringlimits the integration to the Nyquist band only). The result, after somechange of integration variables, is:

$\left\{ T_{l} \right\}_{i,j} = {{1/T_{c}}{\sum\limits_{k}{{h_{0}\left( {\left\lbrack {k + i - j} \right\rbrack T_{c}} \right)} \cdot {h_{0}\left( {kT}_{c} \right)}^{+}}}}$

which yields the edge-effects neglecting approximation of Equation 24.With this approximation, the channel-dependent transformation isactually given (up to a constant gain factor of

$\left. \frac{E\left\{ {{a_{0}(n)}}^{2} \right\}}{\sum\limits_{l = 1}^{L}{N_{l}/T_{c}}} \right)\mspace{14mu} {by}\text{:}$

$\begin{matrix}{\left\lbrack {{H_{0} \cdot H_{0}^{+}} + {\frac{N_{0}}{\sum\limits_{l = 1}^{L_{1}}N_{l}} \cdot I}} \right\rbrack^{- 1} \cdot H_{0}} & \left( {{Equation}\mspace{14mu} 25} \right)\end{matrix}$

If the in-cell MAI is large compared to the white noise modeling theout-of-cell MAI

$\left( {{i.e.},{{\sum\limits_{l = 1}^{L}N_{l}}\operatorname{>>}N_{0}}} \right),$

then Equation 25 reduces to the channel inverse. In such a case, theequalizer of Equation 25 completely restores the orthogonality of theusers' codes that was destroyed by the channel. If, on the other hand,

$\sum\limits_{l = 1}^{L}{N_{l}\mspace{11mu} {\operatorname{<<}N_{0}}}$

(i.e., the white noise representing other-cell interference is thedominant factor), then Equation 25 reduces to a matched filter and thereceiver reduces to the RAKE receiver (which is the optimal receiver forthis setting). For any other setting, the equalizer in Equation 25 (orthe one in Equation 22) will produce the best tradeoff (in the MSEsense) between mitigating same cell interference to the white noisecomponent used for modeling out-of-cell interference.

It should be noted that this CDMA equalization technique can also beimplemented using adaptive filter theory. That is, rather than operatingin a batch-mode over the vector of samples Y _(n), one can operatesequentially and apply an adaptive filter to the received samples.

In some cases, a receiver knows the interference spectrum of other basestations. In such a case, a chip-rate equalizer can be derived whiletaking this additional information into account, leading to improvedperformance. Thus, the following parameters may be used in Equation 1:K=1 (i.e., the receiver is assumed to know only the signature of thedesired user) and L=L₁+L₂>1 (where L₁ equals the number of users in theactive base). It is known that all of the interfering signals from theactive base undergo the same channel as the desired signal. This may beexpressed as:

g _(l)(t)=h ₀(t), l=1, . . . , L₁  (Equation 26)

All of the other users (not from the serving cell) are modeled via thesignals corresponding to l=L₁+1, . . . , L₁+L₂.] It is also assumed thatchip-rate sampling is being used.

The solution in this case is still given by the general framework ofEquations 20-23. To gain some insight into this solution, a simplifiedtwo base-station scenario is assumed. Thus, in addition to Equation 26:

g ₁(t)=h ₁(t), l=L₁+1, . . . , L_(1+L) ₂  (Equation 27)

(i.e., all of the interfering signals from the interfering base undergothe same channel). With this setting and the approximation of Equation24, the following equalizer response is obtained:

$\begin{matrix}{\left\lbrack {{H_{0} \cdot H_{0}^{+}} + {\frac{\sum\limits_{l = {L_{1} + 1}}^{L_{2}}N_{l}}{\sum\limits_{l = 1}^{L_{1}}N_{l}} \cdot H_{1} \cdot H_{1}^{+}} + {\frac{N_{0}}{\sum\limits_{l = 1}^{L_{1}}N_{l}} \cdot I}} \right\rbrack^{- 1} \cdot H_{0}} & \left( {{Equation}\mspace{14mu} 28} \right)\end{matrix}$

If the main source of interference is the same-cell MAI then, as before,Equation 28 reduces to the channel inverse which restores theorthogonality of the signal signatures. If, on the other hand, theother-cell MAI and thermal noise are the dominant factor, then Equation28 reduces to a matched filter in colored noise, which is the optimalsolution in this scenario. In any other setting, the equalizer providesthe best tradeoff between these two receivers.

In order to examine the improvement in performance of the equalizer ofEquation 28 over the conventional practice of Equation 25, a simple twobase station scenario is considered where the desired base stationsignal undergoes a flat fading channel and the interfering base stationsignal undergoes an AR(1) channel model

$\frac{1}{1 - {\alpha \cdot Z^{- 1}}}.$

Suppose that the interfering base station signal is much stronger thanthe desired base station signal. Then, the conventional white noiseequalizer will collapse to a unity transformation (i.e., it will donothing and therefore be identical to a rake receiver), since the activebase station undergoes flat fading. The SNR at the output of thisequalizer would then be:

${S\; N\; R_{{White\_ Noise}{\_ Equalizer}}} = \frac{E\left\{ {S}^{2} \right\}}{E{\left\{ {I}^{2} \right\} \cdot \frac{1}{1 - \alpha^{2}}}}$

where E{|S|²} is the total transmitted power at the desired basestation, E{|I|²} is the total transmit power of the interfering basestation, and

$\frac{1}{1 - \alpha^{2}}$

is the sum of the impulse response squared taps that the interferingsignal undergoes. Now, the colored noise equalizer will take intoaccount the spectral shape of the interfering signal and will generatean equalizer that whitens the noise (recall that E{|S|²}<<E{|I|²}, sothe equalizer will roughly handle only the noise) and is followed by amatched filter to the noise whitener. Hence, with this model, theequalizer's frequency response is:

(1−α·Z ⁻¹)·(1−α·Z)=−α·Z+(1+α²)−α·Z ⁻¹

where the first term, (1−α·Z⁻¹), is the noise whitener inverting thechannel of the interfering BS; the second term, (1−α·Z), is a matchedfilter of the “new” channel that the desired signal now undergoes. TheSNR in this case (ignoring the same cell MAI whose influence is smallunder the assumption that E{|S|²}<<E{|I|²}), may be expressed asfollows:

${S\; N\; R_{{Colored\_ Noise}{\_ Equalizer}}} = \frac{E{\left\{ {S}^{2} \right\} \cdot \left\lbrack {\alpha^{2} + \left( {1 + \alpha^{2}} \right)^{2} + \alpha^{2}} \right\rbrack}}{E{\left\{ {I}^{2} \right\} \cdot \left\lbrack {1 + \alpha^{2}} \right\rbrack}}$

The SNR gain of the colored noise equalization approach over the whitenoise equalizer (and over the RAKE receiver that in this case yields thesame performance as the white noise equalizer) may be obtained bycalculating the ratio of the two SNR expressions:

${SNR\_ Gain}_{{Proposed\_ Colored}{\_ Equalizer}{\_ Approach}} = \frac{1 + {4 \cdot \alpha^{2}} + \alpha^{4}}{1 - \alpha^{4}}$

As can be seen, the SNR gain approaches infinity when α approaches 1(i.e., as the interference channel becomes more colored (deeper null),the SNR gain increases). Although discussed in the context of arelatively simple example, it can be shown that the same conclusionholds in more complex cases.

A better comparison would take the same cell MAI into account. In thiscase, the SNR of the white noise equalizer will not change, but thecolored noise equalizer SNR would be:

${S\; N\; R_{{Colored\_ Noise}{\_ Equalizer}}} = \frac{E{\left\{ {S}^{2} \right\} \cdot \left( {1 + \alpha^{2}} \right)^{2}}}{{E{\left\{ {I}^{2} \right\} \cdot \left\lbrack {1 + \alpha^{2}} \right\rbrack}} + {{2 \cdot E}{\left\{ {S}^{2} \right\} \cdot \alpha^{2}}}}$

The SNR gain in this scenario (over the white noise equalizer and theRAKE receiver) would be:

${SNR\_ Gain}_{{Proposed\_ Colored}{\_ Equalizer}{\_ Approach}} = \frac{\left( {1 + \alpha^{2}} \right)^{2}}{1 - \alpha^{4} + {2{\frac{E\left\{ {S}^{2} \right\}}{E\left\{ {I}^{2} \right\}} \cdot \alpha^{2}}}}$

Again, when α approaches 1, the SNR gain approaches infinity (note thatthis solution is for the case E{|S|²}<<E{|I|²}). Other cases can beanalyzed similarly.

To achieve a linear MMSE MUD receiver architecture, the followingparameters may be used in Equation 1: K=k₁+k₂, . . . , k_(B) (wherek_(i) is the number of users of the i-th base) and L=1. Also, it may beassumed that:

$\begin{matrix}\begin{matrix}{{{h_{l}(t)} = {{\overset{\sim}{h}}_{1}(t)}},} & {{l = 1},\ldots \mspace{14mu},k_{1}} \\{{{h_{l}(t)} = {{\overset{\sim}{h}}_{2}(t)}},} & {{l = {k_{1} + 1}},\ldots \mspace{14mu},{k_{1} + k_{2}}} \\\vdots & \; \\{{{h_{l}(t)} = {{\overset{\sim}{h}}_{B}(t)}},} & {{l = {{\sum\limits_{i = 1}^{B - 1}k_{i}} + 1}},\ldots \mspace{14mu},{\sum\limits_{i = 1}^{B}k_{i}}}\end{matrix} & \left( {{Equation}\mspace{14mu} 29} \right)\end{matrix}$

where {tilde over (h)}_(i)(t) denotes the channel between the i-th basestation and the desired user. In this setting, the solution coincideswith the full blown linear MMSE solution for the cellular down-linkenvironment.

A complexity analysis performed for the full-blown linear MMSE multiuserdetector described above revealed that its most computationallyintensive portion is the generation of the matrix E{ Y· Y ⁺}, and notits inversion. Furthermore, this computational complexity is in directproportion to the number of users in the system. This suggests thefollowing efficient cost-performance compromise that uses MUD for someusers and equalization for the rest. In at least one embodiment, thestrongest K users in the system are selected and these K users areeffectively handled by a linear MMSE MUD. For these users, h_(l)(t) areassigned in a similar manner to Equation 29 above. The remaining L−1users, who are less important as they are weaker (normally each of theseusers contributes less to the total interference than any of the Kstrongest users), are effectively treated using lower complexityequalization. For these weaker users, g_(l)(t) are assigned in a similarmanner to Equation 29 above.

To achieve such a receiver architecture, the parameters in Equation 1may be selected as follows: K is a user definable parameter thatdetermines the cost-performance tradeoff and L=the total number ofusers−K+1. Also, it may be assumed that:

h _(l)(t)={tilde over (h)}_(i)(t) or g _(l)(t)={tilde over(h)}_(i)(t)  (Equation 30)

where the l-th user is assumed to belong to the i-th base station and,as before, {tilde over (h)}_(i)(t) denotes the channel between the i-thbase station and the desired user. All in all, this approach provides avery flexible and attractive cost-performance tradeoff. Withsufficiently large values of K, the performance converges to that of thefull-blown MMSE MUD. Yet, for smaller values of K accompanied by anefficient mechanism for choosing the strongest (most interfering) users,similar performance may be achieved at a much lower complexity.

In at least one embodiment of the invention, a symbol level approach isutilized (i.e., a receiver based on a bank of matched-filter outputs,rather than on received samples). In order to consider the symbol-levelapproach, both sides of Equation 12 are multiplied by some K×(m₂−m+1)rectangular matrix A (which, in general, may be time varying), asfollows:

X _(n) ≡A· Y _(n) =A·P _(eq)(n)·ā(n)+A·ē(n)  (Equation 31)

The special choice of A=P⁺ _(eq) yields the bank of matched filteroutputs, but other choices are possible. The MMSE solution now followsdirectly from the previous derivation as follows:

$\begin{matrix}{{{\hat{a}}_{0}(n)} = {{\sum\limits_{k = 1}^{K}{{b_{k}^{*}(n)} \cdot {x_{k}(n)}}} = {{\overset{\_}{B}}_{n}^{+} \cdot {\overset{\_}{X}}_{n}}}} & \left( {{Equation}\mspace{14mu} 32} \right)\end{matrix}$

and the MMSE solution is:

B _(n) =E{A· Y _(n)· Y _(n) ⁺ ·A ⁺}⁻¹ ·E{A· Y _(n) ·a ₀(n)*}  (Equation33)

The solution is now given by substituting Equation 7 and Equation 13into Equation 33. In those cases where the matrix inversion is acomputational bottle-neck, the above transformation reduces thedimensionality from m₂−m₁+1 to K, which may be much smaller. With largevalues of K, the performance would be close to that of the full-blownMMSE MUD. Also, with smaller values of K accompanied by an efficientmechanism for choosing the strongest users, the above receiver couldprovide similar performance to the MMSE MUD at a much lower complexity.

In some applications, multi-code transmission may be employed (i.e., asingle user is assigned multiple code signatures, often in order toincrease this user's throughput). In order to reduce the computationalcomplexity, it may be desirable to represent the receiver as onefront-end filter (that may be time-varying) that is common to all codesand then a conventional bank of despreaders (with each despreader tunedto a specific code). This structure follows immediately from Equation 6,Equation 7, and Equation 13 revealing that the basic operation E{ Y_(n)· Y _(n) ⁺}⁻¹ is common to all codes and needs to be followed by E{Y _(n)·a₀(n)*} (which is, of course, code specific).

The above-described family of receivers could easily be combined with aconventional RAKE receiver. To see this, it is noted that thecross-correlation term of Equation 7 (that appears in the generalreceiver structure of Equation 6) is in fact a RAKE receiver. FIG. 3 isa block diagram illustrating an example receiver arrangement 50 that maybe used to represent the family of MUD receivers in accordance with anembodiment of the present invention. As illustrated, the receiverarrangement 50 includes: an antenna 52, an RF to baseband subsystem 54,a spectral whitening function 56, a RAKE receiver 58, and a channeldecoder 60. The antenna 52, RF to baseband subsystem 54, and channeldecoder 60 operate in substantially the same manner describedpreviously. The spectral whitening function 56 processes the basebandsamples output by the RF to baseband subsystem 54 in accordance with thefirst term on the right side of Equation 6. The resulting signal isdelivered to the RAKE receiver 58 which processes it in accordance withthe second term on the right side of Equation 6. The output of the RAKEreceiver 58 is delivered to the channel decoder 18 for decoding. Theabove-described technique may be useful when upgrading an existingsystem that already builds upon a RAKE receiver.

In some cases it may be desirable to combine the proposed approach withinterference cancellation. This may be the case, for example, when theinterference cancellation can be performed utilizing the coding gain ofthe channel code (e.g., a convolution code or a turbo code). As anexample of one possible implementation, consider the High Speed DownlinkPacket Access (HSDPA) channel of wideband CDMA (WCDMA), where all thetraffic signature codes of a base station may be allocated to one userat a particular point in time. Since the user is capable of decoding itsown transmission, but normally not those of other users, it makes senseto try to perform coded interference cancellation on the same cell MAIand to treat all of the other MAI components using the aboveequalization and/or MUD approach. The optimal solution in this case(assuming perfect interference cancellation) is obtained by simplyremoving the components associated with the signals to be cancelled fromthe received signal. For example, in Equation 28 above, canceling theMAI of the desired cell amounts to replacing

$\sum\limits_{l = 1}^{L_{1}}{N_{l}\mspace{14mu} {by}\mspace{14mu} {N_{1}.}}$

This corresponds to the optimal chip-rate equalizer under the assumptionthat the MAI associated with the signals n₂(t), n₃(t), . . . , n_(L) ₁(t) are perfectly canceled. In HSDPA applications, these signals wouldmerely be the result of multi-code transmission to the desired user.After employing the turbo-decoding (and utilizing its coding gain), theoriginal transmitted information symbols can be estimated and used forinterference cancellation based on conventional re-encoding,re-modulation, and subtraction techniques.

In one embodiment, this may be done iteratively. First, the weights arecalculated as described above and all of the multi-codes aredemodulated. Then, the total symbol stream (containing the symbols fromall multi-code signals) is fed into the channel decoder. The output ofthe channel decoder is then re-encoded, interleaved and re-modulated togenerate replicas of the multi-code signals. Next, the seconddemodulation iteration is performed, with the replicas of all themulti-codes, excluding the one being demodulated, being subtracted fromthe received signal. Thus, in this second demodulation iteration, theinterference effects of the other multi-code signals is reducedresulting in improved demodulation performance. Again, the demodulatedsignals of the multi-codes are fed into the channel decoder whose outputis used to construct a replica of the multi-code signals, and theprocess continues iteratively until a certain stopping criteria issatisfied. At each iteration, soft information (i.e., symbol reliabilityinformation) from the channel decoder output can be combined into thereplica generation and subtraction mechanism so that symbols with lowreliability are only partially subtracted (and vice-versa) in order toimprove the convergence properties of this approach. This technique maybe particularly suitable for turbo codes, where the decoding mechanismis iterative by nature and involves soft metric (symbol reliability)calculations. FIG. 4 is a block diagram illustrating an example receiverarrangement 70 in accordance with an embodiment of the presentinvention, that may be used to implement interference cancellationtechniques. As illustrated, the receiver arrangement 70 may include atleast one of: an antenna 72, an RF to baseband subsystem 74, a jointequalization and MUD unit 76, and a channel decoder 78. The antenna 72,the RF to baseband subsystem 74, the joint equalization and MUD unit 76,and the channel decoder 78 may operate in a similar manner to thecomponents described previously in connection with FIG. 1. In addition,a feedback path 68 is provided to feed back decoded information from thechannel decoder 78 for re-encoding, interleaving, re-modulation, andinterference subtraction within the joint equalization and MUD unit 76or elsewhere within the receiver arrangement 70. As described above,this may be performed as an iterative process.

FIG. 5 is a block diagram illustrating an example method 80 for use in aCDMA receiver in accordance with an embodiment of the present invention.Active users in a vicinity of a receiver are first assigned to either afirst group or a second group (block 82). The term “active user” is usedherein to denote a user that is presently communicating in a system andthat will be considered during a detection process for a desired user.An active user may be associated with the serving base station oranother base station. Users assigned to the first group may include, forexample, users whose signature sequences are assumed to be known to thereceiver and users assigned to the second group may include users whosesignature sequences are assumed unknown to the receiver. Any of avariety of different user assignment criteria may be used to assign theusers. In addition, the assignment criterion that is used may be userdefinable.

A joint MMSE equalization/MUD despreading sequence is next generatedbased on the distribution of active users between the first and secondgroups (block 84). This may be performed, for example, as describedpreviously where various receiver architectures are achieved by usingdifferent values of K and L. A received CDMA signal is subsequentlyprocessed using the joint MMSE equalization/MUD despreading sequence(block 86). This may include, for example, multiplying baseband samplesby the despreading sequence and then accumulating the results in anaccumulator to de-spread the desired data. The type of processing thatwill be carried out using the joint MMSE equalization/MUD despreadingsequence will typically depend upon how the active users were originallyassigned. For example, when the first group includes only the desireduser and the second group includes no users, the CDMA signal may beprocessed as in a RAKE receiver. When the first group includes allactive users and the second group includes no users, the CDMA signal maybe processed as in an MMSE MUD. When the first group includes only thedesired user and the second group includes all other active usersassociated with the same base station as the desired user, the CDMAsignal may be processed as in an MMSE equalizer. When the first andsecond groups each include multiple users, a combination of MMSEequalization and MMSE MUD processing may be performed. Otherarrangements may alternatively be used.

In some embodiments, multiple receive antennas may be used. The abovederivations may be extended to the multiple antenna case in astraightforward manner. In particular, the general solution of Equation6 still holds but all vectors should be augmented to include themultiple antenna signals. For example, in the two antenna case, W _(n)should include the two antenna weight vectors W ⁽¹⁾ _(n) and W ⁽²⁾ _(n)and the received signal should include the two antenna signals Y ⁽¹⁾_(n) and Y ⁽²⁾ _(n).

In the foregoing detailed description, various features of the inventionare grouped together in one or more individual embodiments for thepurpose of streamlining the disclosure. This method of disclosure is notto be interpreted as reflecting an intention that the claimed inventionrequires more features than are expressly recited in each claim. Rather,as the following claims reflect, inventive aspects may lie in less thanall features of each disclosed embodiment.

Although the present invention has been described in conjunction withcertain embodiments, it is to be understood that modifications andvariations may be resorted to without departing from the spirit andscope of the invention as those skilled in the art readily understand.Such modifications and variations are considered to be within thepurview and scope of the invention and the appended claims.

1. (canceled)
 2. An apparatus comprising: a despreader to despread datawithin a baseband code division multiple access (CDMA) signal, said databeing associated with a desired user; and a despreading sequencegenerator to generate a joint equalization/multi-user detection (MUD)despreading sequence for use by said despreader to despread said data.3. The apparatus of claim 2, wherein: said despreading sequencegenerator treats active users as being within one of two groups.
 4. Theapparatus of claim 3, wherein: said despreading sequence generatorgenerates said joint equalization/MUD despreading sequence based on aquantity of users in said first group and a quantity of users in saidsecond group.
 5. The apparatus of claim 3, wherein: said apparatusprocesses user signals associated with users in said first group usingMUD-type processing and user signals associated with users in saidsecond group using equalizer-type processing.
 6. The apparatus of claim3, wherein: said apparatus behaves as a RAKE receiver when a quantity ofusers in said first group is 1 and a quantity of users in said secondgroup is
 0. 7. The apparatus of claim 3, wherein: said apparatus behavesas a minimum mean square error (MMSE) equalizer when said first groupincludes only said desired user and said second group includes all otherusers associated with the same base station as said desired user.
 8. Theapparatus of claim 3, wherein: said apparatus behaves as a multi-userdetector (MUD) when said first group includes all active users and saidsecond group includes no users.
 9. The apparatus of claim 3, wherein:active users are assigned to said first and second groups based on apredetermined assignment criterion.
 10. The apparatus of claim 9,wherein: said predetermined assignment criterion is user-definable. 11.The apparatus of claim 9, wherein: said predetermined assignmentcriterion places users associated with a serving base station withinsaid first group and users associated with other base stations withinsaid second group.
 12. The apparatus of claim 9, wherein: saidpredetermined assignment criterion places users having stronger receivedsignals within said first group and users having weaker received signalswithin said second group.
 13. The apparatus of claim 2, furthercomprising: a chip rate sampler to sample said baseband CDMA signal at achip rate before said signal reaches said despreader.
 14. The apparatusof claim 2, further comprising: a channel decoder to decode an output ofsaid despreader.
 15. The apparatus of claim 14, further comprising: afeedback path from an output of said channel decoder to allow decodedinformation to be re-encoded, interleaved, and re-modulated for use ininterference cancellation.
 16. A method for use in connection with acode division multiple access (CDMA) receiver, comprising: assigningindividual active users to either a first group or a second group, andgenerating a joint minimum mean square error (MMSE) equalization andmulti-user detection (MUD) despreading sequence based on a distributionof active users within said first and second groups; processing areceived CDMA signal using said joint MMSE equalization and MUDdespreading sequence including performing MMSE MUD processing when saidfirst group includes all active users and said second group includes nousers.
 17. The method of claim 16, wherein: said first group includesusers whose signature sequences are assumed known to a receiver and saidsecond group includes users whose signature sequences are assumedunknown to the receiver.
 18. The method of claim 16, wherein: assigningindividual active users includes assigning users based upon apredetermined assignment criterion.
 19. The method of claim 18, wherein:said predetermined assignment criterion is user definable.
 20. Themethod of claim 16, wherein: assigning individual active users includesassigning users associated with a serving base station to said firstgroup and assigning users associated with other base stations to saidsecond group.
 21. The method of claim 16, wherein: assigning individualactive users includes assigning users to said first and second groupsbased on received signal strength.
 22. The method of claim 16, wherein:processing includes performing RAKE receiver processing on said CDMAsignal when said first group includes only a desired user and saidsecond group includes no users.
 23. The method of claim 16, wherein:processing includes performing MMSE equalization when said first groupincludes only said desired user and said second group includes all otheractive users associated with the same base station as said desired user.24. The method of claim 16, wherein: processing includes performing acombination of MMSE equalization and MMSE MUD processing when both saidfirst group and said second group include multiple users.
 25. An articlecomprising a storage medium having instructions stored thereon that,when executed by a computing platform, result in: assigning, within acode division multiple access (CDMA) receiver, individual active usersto either a first group or a second group; and generating a jointminimum mean square error (MMSE) equalization and multi-user detection(MUD) despreading sequence based on a distribution of active userswithin said first and second groups.
 26. The article of claim 25,wherein: said first group includes users whose signature sequences areassumed known to the CDMA receiver and said second group includes userswhose signature sequences are assumed unknown to the CDMA receiver. 27.The article of claim 25, wherein said instructions, when executed bysaid computing platform, further result in: processing a received CDMAsignal using said joint MMSE equalization and MUD despreading sequence.28. A system comprising: multiple receive antennas to receive a codedivision multiple access (CDMA) signal from a wireless channel; adespreader to despread data within a baseband version of said CDMAsignal, said data being associated with a desired user; and adespreading sequence generator to generate a jointequalization/multi-user detection (MUD) despreading sequence for use bysaid despreader to despread said data.
 29. The system of claim 28,wherein: said despreading sequence generator treats active users asbeing within one of a first group and a second group.
 30. The system ofclaim 29, wherein: said despreading sequence generator generates saidjoint equalization/MUD despreading sequence based on a quantity of usersin said first group and a quantity of users in said second group. 31.The system of claim 29, wherein: said system processes user signalsassociated with said first group using MUD type processing and usersignals associated with users in said second group using equalizer-typeprocessing.
 32. The system of claim 28, further comprising: a chip ratesampler to sample said baseband version of said CDMA signal at a chiprate before it reaches said despreader.
 33. A method comprising:receiving a code division multiple access (CDMA) signal from a wirelesschannel; and detecting user data within said CDMA signal, whereindetecting user data includes processing said CDMA signal using acombination of minimum mean square error (MMSE) equalization and MMSEmulti-user detection (MUD) techniques.
 34. The method of claim 33,wherein: processing said CDMA signal includes: obtaining a joint MMSEequalization and multi-user detection (MUD) despreading sequence; anddespreading said user data within said CDMA signal using said joint MMSEequalization and MUD despreading sequence.
 35. The method of claim 34,comprising: channel decoding said user data after said despreadinig togenerate decoded data; and using at least some of said decoded data toperform interference cancellation.
 36. The method of claim 33,comprising: converting said CDMA signal from a radio frequency (RF)representation to a baseband representation before said processing. 37.The method of claim 36, comprising: sampling said basebandrepresentation of said CDMA signal at a chip rate before saidprocessing.